Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation

TL;DR

This paper introduces an adaptive time series forecasting framework that detects data drift using PELT and retrains models selectively, significantly improving accuracy across real-world and synthetic datasets.

Contribution

It presents a novel integration of PELT-based drift detection with targeted model retraining for improved forecasting in dynamic environments.

Findings

44% reduction in MAE on real-world data

39% increase in R^2 on real-world data

Significant accuracy improvements on synthetic data

Abstract

Accurate time series forecasting models are often compromised by data drift, where underlying data distributions change over time, leading to significant declines in prediction performance. To address this challenge, this study proposes an adaptive forecasting framework that integrates drift detection with targeted model retraining to compensate for drift effects. The framework utilizes the Pruned Exact Linear Time (PELT) algorithm to identify drift points within the feature space of time series data. Once drift intervals are detected, selective retraining is applied to prediction models using Multilayer Perceptron (MLP) and Lasso Regressor architectures, allowing the models to adjust to changing data patterns. The effectiveness of the proposed approach is demonstrated on two datasets: a real-world dataset containing electricity consumption and HVAC system data, and a synthetic financial dataset designed to test cross-domain applicability. Initial baseline models were developed without drift detection using extensive feature engineering. After integrating drift-aware retraining, the MLP model achieved a 44% reduction in mean absolute error (MAE) and a 39% increase in R^2 on the real-world dataset, while even greater improvements were observed on the synthetic financial dataset. Similar enhancements were achieved with the Lasso Regressor. These results highlight the robustness and generalizability of incorporating drift detection and adaptive retraining to sustain forecasting accuracy across diverse domains.